Create app.py

app.py

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+import tensorflow as tf
+import numpy as np
+import gradio as gr
+import cv2 # opencv-python
+
+
+model = tf.keras.models.load_model("/content/TP_MNIST_CNN_model.h5")
+
+
+def preprocess_image(img):
+    img = tf.image.rgb_to_grayscale(img)  # Convert to grayscale
+    img = tf.image.resize(img, (28, 28))   # Resize to model input size
+    img = img / 255.0                      # Normalize pixel values
+    img = np.expand_dims(img, axis=0)      # Add batch dimension
+    return img
+
+# Function to make predictions
+def predict_image(img):
+    processed_img = preprocess_image(img)
+    prediction = model.predict(processed_img)
+    return {str(i): float(prediction[0][i]) for i in range(10)}
+
+gr.Interface(
+    fn=predict_image, 
+    inputs=gr.Image(),  
+    outputs=gr.Label(num_top_classes=3), 
+    examples=["minst6.png", "mnist1.png", "mnist2_5.png", "mnist69.png"],  # Example images for interface
+    title='Handwritten Digits Classification stage DREAMS Team'  
+).launch(share=True)